Slide 14
Slide 14 text
COLOR IMAGE QUANTIZATION
FOR
FRAME BUFFER DISPLAY
Paul Heckbert
Computer Graphics Lab
New York Institute of Technology
ABSTRACT
Algorithms for adaptive, tapered quant-
ization of color images are described.
The research is motivated by the desire to
display high-quality reproductions of
color images with small frame buffers. It
is demonstrated that many color images
which would normally require a frame buffer
having 15 bits per pixel can be qua ntized
to 8 or fewer bits per pixel with little
subjective degradation. In most cases,
the resulting images look significantly
better than those made with uniform quant-
ization.
The color image quantization task is
broken into four phases:
i) Sampling the original image for color
statistics
2) Choosing a colormap based on the
color statistics
3) Mapping original colors to their
nearest neighbors in the colormap
4) Quantizing and redrawing the original
image (with optional dither).
Several algorithms for each of phases
2-4 are described, and images created by
each given.
CR CATEGORIES: II.3.3 (Information Stor-
age and Retrieval): Information Search and
Retrieval - clustering; search process;
1.3.3 (Computer Graphics): Picture/Image
Generation - digitization and scanning;
display algorithms; 1.4.1 (Image Process-
ing) : Digitization - quantization.
General Terms: Algorithms.
Additional Key Words and Phrases: dither.
Permission to copy without fee all or part of this material is granted
provided that the copies are not made or distributed for direct
commercial advantage, the ACM copyright notice and the title of the
publication and its date appear, and notice is given that copying is by
permission of the Association for Computing Machinery. To copy
otherwise, or to republish, requires a fee and/or specific permission.
1982 ACM 0-89791-076-1/82/007/0297 $00.75
INTRODUCTION
The power and versatility of frame
buffers has created an increasing demand
for them in industry, education, and the
home. Most of these frame buffers are
capable of displaying a static color image,
yet many of them do not contain the amount
of memory necessary to match the spatial
and color resolution of the human eye.
The eye is capable of distinguishing at
least fifty thousand colors [15]. There-
fore, it would take a frame buffer with at
least ]5 bits per pixel to reproduce and
display a color image with no noticeable
contouring. On smaller frame buffers,
contouring effects can become objectionable.
One way to eliminate some of this quant-
ization error is to employ the method of
tapered quantization.
The purpose of this paper is to explore
techniques for color image quantization
with the goal of high-quality image display
on frame buffers.
The Original Image
Our input data are the red, green, and
blue separations of a digitized color
image. A typical form for the input image
is a rectangular array of pixels each hav-
ing 24 bits (8 bits per component). The
color components are usually represented
by numbers in the range [0,255]. If the
original image is in this form, then
strictly speaking it has already been
quantized (when it was digitized from a
video signal, for instance). We will
assume that this initial quantization does
not cause perceptible quantization errors.
This will be the case if (a) the full gamut
of RGB space is used, that is, if the
digitization equipment is set up so that
black is quantized to (r,g,b)=(0,0,0),
white to (255,255,255), red to (255,0,0),
etc. and (b) the 256 levels are approx-
imately equally spaced perceptually.
Given these conditions, we can regard the
24-bit original image as the "true" image.
We will try to approximate it as closely
as possible when we quantize.
fig. 14:24 bit original
image of "Surface" (the
surface of the RGB color
cube unrolled).
fig. 15: popularity algorithm,
256 colors.
fig. 16: median cut, 256
colors.
fig. 17: median cut with
dither, 256 colors.
fig. 18: exploded view of
16 tapered quantization
cells in the RGB cube.